Machine Learning Lead

Responsibilities:

• Define and drive the ML roadmap across the product ecosystem, aligning model development with product and business priorities.
• Lead the design of custom ML models from scratch for digital signal processing, audio signal analysis, and accelerometer data, with published papers or demonstrable custom-built models expected.
• Apply advanced techniques, time series analysis, CNNs, RNNs, GANs, LLMs, and low-rank adaptation to solve hard problems in pet wellness technology.
• Build, lead, and mentor a team of ML engineers, setting technical standards and a culture of rigorous experimentation.
• Own sophisticated data analysis and preprocessing pipelines that prepare datasets for ML at scale.
• Establish MLOps practices and infrastructure that get models into production reliably, at scale.
• Set benchmarks for continuously monitoring and optimizing model accuracy and efficiency.
• Partner closely with product, engineering, and data science leadership to ship ML into Hoomanely's product ecosystem.
• Stay ahead of the field in ML and signal processing, and drive a culture of research and innovation at Hoomanely.

Requirements:

• You have a B. Tech in Computer Science, Data Science, Electrical Engineering, or a related field from a Tier 1 institute (IIT, NIT, BITS Pilani, or equivalent), which is good to have.
• You have 10+ years of experience building custom ML models, including for digital signal processing, audio signal analysis, or accelerometer data.
• You've led and grown ML teams, with a track record of taking models from research into production.
• You have strong depth in time-series ML, CNNs, RNNs, GANs, LLMs, and low-rank adaptation techniques.
• You have deep MLOps experience, deploying and running ML models in production.
• You're fluent in TensorFlow, PyTorch, and Python.
• You bring strong analytical, problem-solving, communication, and leadership skills.

Bonus:

• Experience in pet wellness or a related industry.
• Familiarity with IoT device data processing and analysis.
• Knowledge of cloud platforms for ML deployment.
• Published research or patents in applied machine learning.